HyperCard applications for teaching information systems
SIGCSE '91 Proceedings of the twenty-second SIGCSE technical symposium on Computer science education
The design, implementation, and use of DSTutor: a tutoring system for denotational semantics
SIGCSE '91 Proceedings of the twenty-second SIGCSE technical symposium on Computer science education
Foundations of genetic algorithms
Foundations of genetic algorithms
DBTool: a graphical database design tool for an introductory database course
SIGCSE '92 Proceedings of the twenty-third SIGCSE technical symposium on Computer science education
A graphical computer simulator for systems programming courses
SIGCSE '92 Proceedings of the twenty-third SIGCSE technical symposium on Computer science education
Designing interactive visualization tools for the graphics classroom
SIGCSE '92 Proceedings of the twenty-third SIGCSE technical symposium on Computer science education
LibGA: a user-friendly workbench for order-based genetic algorithm research
SAC '93 Proceedings of the 1993 ACM/SIGAPP symposium on Applied computing: states of the art and practice
Manipulating subpopulations of feasible and infeasible solutions in genetic algorithms
SAC '93 Proceedings of the 1993 ACM/SIGAPP symposium on Applied computing: states of the art and practice
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
The use of animation to explain genetic algorithms
SIGCSE '97 Proceedings of the twenty-eighth SIGCSE technical symposium on Computer science education
Using Java to develop Web based tutorials
SIGCSE '98 Proceedings of the twenty-ninth SIGCSE technical symposium on Computer science education
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In this paper we discuss the design and implementation of GATutor, a graphical tutorial system for genetic algorithms (GA). The X Window/Motif system provides powerful tools for the development of a user interfaces with a familiar feel and look. We implemented the Traveling Salesman Problem (TSP) and the Set Covering Problem (SCP) as two example GA problems in the tutorial. The TSP problem uses an order-based chromosome representation (permutation of n objects), while the SCP uses bit strings. The user has numerous buttons to select the GA parameters. These include (a) type of initial population: random or from a file, (b) mode: steady-state or generational, (c) population size, (d) maximum number of generations or trials, (e) generation gap, (f) selection mode, (g) selection bias, (h) selection of the crossover operation from a choice of several possibilities, (i) mutation method, (j) mutation rate, (k) replacement method, (l), elitism, etc. The user has the ability to do astep by step execution or to do a continuous run. The screen layout provides visual representation of the chromosomes in the population with the ability to scroll. This gives the user the option of varying one or two GA parameters to visually see the effect on the algorithm. One of most important features of this tutorial is the set of help screens that explain, with examples, all of the options for each of the GA parameters. This package has already been very useful for teaching the fundamental features of GAs in many different courses, and it has been very valuable in our GA research projects.